code: def distributed_item_sampler_subprocess(
      proc_id,
      nprocs,
      item_set,
      num_ids,
      num_workers,
      batch_size,
      drop_last,
      drop_uneven_inputs,
  ):
      # On Windows, the init method can only be file.
      init_method = (
          f"file:///{os.path.join(os.getcwd(), 'dis_tempfile')}"
          if platform == "win32"
          else "tcp://127.0.0.1:12345"
      )
      dist.init_process_group(
          backend="gloo",  # Use Gloo backend for CPU multiprocessing
          init_method=init_method,
          world_size=nprocs,
          rank=proc_id,
      )
  
      # Create a DistributedItemSampler.
      item_sampler = gb.DistributedItemSampler(
          item_set,
          batch_size=batch_size,
          shuffle=True,
          drop_last=drop_last,
          drop_uneven_inputs=drop_uneven_inputs,
      )
      feature_fetcher = gb.FeatureFetcher(
          item_sampler,
          gb.BasicFeatureStore({}),
          [],
      )
      data_loader = gb.DataLoader(
          feature_fetcher, num_workers=num_workers
      )
  
      # Count the numbers of items and batches.
      num_items = 0
      sampled_count = torch.zeros(num_ids, dtype=torch.int32)
      for i in data_loader:
          # Count how many times each item is sampled.
          sampled_count[i.seed_nodes] += 1
          if drop_last:
              assert i.seed_nodes.size(0) == batch_size
          num_items += i.seed_nodes.size(0)
      num_batches = len(list(item_sampler))
  
      if drop_uneven_inputs:
          num_batches_tensor = torch.tensor(num_batches)
          dist.broadcast(num_batches_tensor, 0)
          # Test if the number of batches are the same for all processes.
          assert num_batches_tensor == num_batches
  
      # Add up results from all processes.
      dist.reduce(sampled_count, 0)
  
      try:
          # Make sure no item is sampled more than once.
          assert sampled_count.max() <= 1
      finally:
          dist.destroy_process_group()
  
imports: import os
  import re
  from sys import platform
  import dgl
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  from dgl import graphbolt
  from torch.testing import assert_close
label_desc: Use image node_id 17 for calling a global function with example usage: distributed_item_sampler_subprocess(proc_id, nprocs, item_set, num_ids, num_workers, batch_size, drop_last, drop_uneven_inputs) without return types